Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations1696234
Missing cells10226144
Missing cells (%)23.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 GiB
Average record size in memory920.9 B

Variable types

Numeric12
DateTime3
Text6
Categorical5

Alerts

Unnamed: 0 is highly overall correlated with player_idHigh correlation
competition_id is highly overall correlated with current_club_domestic_competition_idHigh correlation
current_club_domestic_competition_id is highly overall correlated with competition_idHigh correlation
current_club_id is highly overall correlated with player_club_id and 1 other fieldsHigh correlation
highest_market_value_in_eur is highly overall correlated with market_value_in_eurHigh correlation
last_season is highly overall correlated with market_value_in_eurHigh correlation
market_value_in_eur is highly overall correlated with highest_market_value_in_eur and 1 other fieldsHigh correlation
player_club_id is highly overall correlated with current_club_id and 1 other fieldsHigh correlation
player_current_club_id is highly overall correlated with current_club_id and 1 other fieldsHigh correlation
player_id is highly overall correlated with Unnamed: 0High correlation
position is highly overall correlated with sub_positionHigh correlation
sub_position is highly overall correlated with positionHigh correlation
first_name has 639134 (37.7%) missing values Missing
last_name has 639134 (37.7%) missing values Missing
name has 639134 (37.7%) missing values Missing
last_season has 639134 (37.7%) missing values Missing
current_club_id has 639134 (37.7%) missing values Missing
country_of_citizenship has 639134 (37.7%) missing values Missing
date_of_birth has 639134 (37.7%) missing values Missing
sub_position has 639134 (37.7%) missing values Missing
position has 639134 (37.7%) missing values Missing
foot has 639134 (37.7%) missing values Missing
height_in_cm has 639134 (37.7%) missing values Missing
contract_expiration_date has 639134 (37.7%) missing values Missing
current_club_domestic_competition_id has 639134 (37.7%) missing values Missing
current_club_name has 639134 (37.7%) missing values Missing
market_value_in_eur has 639134 (37.7%) missing values Missing
highest_market_value_in_eur has 639134 (37.7%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
goals has 1550716 (91.4%) zeros Zeros
assists has 1577530 (93.0%) zeros Zeros

Reproduction

Analysis started2025-03-12 19:11:15.317045
Analysis finished2025-03-12 19:12:28.640726
Duration1 minute and 13.32 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct1696234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean848116.5
Minimum0
Maximum1696233
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:12:28.692150image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile84811.65
Q1424058.25
median848116.5
Q31272174.8
95-th percentile1611421.3
Maximum1696233
Range1696233
Interquartile range (IQR)848116.5

Descriptive statistics

Standard deviation489660.72
Coefficient of variation (CV)0.57735078
Kurtosis-1.2
Mean848116.5
Median Absolute Deviation (MAD)424058.5
Skewness-3.935866 × 10-15
Sum1.438604 × 1012
Variance2.3976762 × 1011
MonotonicityStrictly increasing
2025-03-12T21:12:28.769678image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
1130818 1
 
< 0.1%
1130828 1
 
< 0.1%
1130827 1
 
< 0.1%
1130826 1
 
< 0.1%
1130825 1
 
< 0.1%
1130824 1
 
< 0.1%
1130823 1
 
< 0.1%
1130822 1
 
< 0.1%
1130821 1
 
< 0.1%
Other values (1696224) 1696224
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
1696233 1
< 0.1%
1696232 1
< 0.1%
1696231 1
< 0.1%
1696230 1
< 0.1%
1696229 1
< 0.1%
1696228 1
< 0.1%
1696227 1
< 0.1%
1696226 1
< 0.1%
1696225 1
< 0.1%
1696224 1
< 0.1%

player_id
Real number (ℝ)

High correlation 

Distinct25661
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208378.44
Minimum10
Maximum1380876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:12:28.833982image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15452
Q158252
median148396
Q3300168
95-th percentile603149
Maximum1380876
Range1380866
Interquartile range (IQR)241916

Descriptive statistics

Standard deviation192896.91
Coefficient of variation (CV)0.92570471
Kurtosis2.1678484
Mean208378.44
Median Absolute Deviation (MAD)103452
Skewness1.4389208
Sum3.534586 × 1011
Variance3.7209217 × 1010
MonotonicityNot monotonic
2025-03-12T21:12:28.896384image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38253 605
 
< 0.1%
32467 578
 
< 0.1%
59561 563
 
< 0.1%
125781 563
 
< 0.1%
74229 560
 
< 0.1%
36139 558
 
< 0.1%
28396 558
 
< 0.1%
56416 551
 
< 0.1%
91845 549
 
< 0.1%
65278 545
 
< 0.1%
Other values (25651) 1690604
99.7%
ValueCountFrequency (%)
10 136
< 0.1%
26 152
< 0.1%
65 122
< 0.1%
77 4
 
< 0.1%
80 12
 
< 0.1%
109 41
 
< 0.1%
123 7
 
< 0.1%
132 77
< 0.1%
215 109
< 0.1%
258 19
 
< 0.1%
ValueCountFrequency (%)
1380876 1
 
< 0.1%
1378362 1
 
< 0.1%
1358447 1
 
< 0.1%
1310513 3
 
< 0.1%
1309326 3
 
< 0.1%
1306851 8
< 0.1%
1302421 5
< 0.1%
1294052 11
< 0.1%
1294049 5
< 0.1%
1294048 2
 
< 0.1%

player_club_id
Real number (ℝ)

High correlation 

Distinct1064
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3124.6329
Minimum1
Maximum116786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:12:28.959607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27
Q1289
median826
Q32441
95-th percentile16239
Maximum116786
Range116785
Interquartile range (IQR)2152

Descriptive statistics

Standard deviation8249.8953
Coefficient of variation (CV)2.6402767
Kurtosis37.534735
Mean3124.6329
Median Absolute Deviation (MAD)626
Skewness5.5674082
Sum5.3001085 × 109
Variance68060772
MonotonicityNot monotonic
2025-03-12T21:12:29.025274image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
418 10257
 
0.6%
131 10166
 
0.6%
368 9983
 
0.6%
506 9644
 
0.6%
985 9631
 
0.6%
281 9571
 
0.6%
631 9554
 
0.6%
31 9443
 
0.6%
371 9358
 
0.6%
27 9324
 
0.5%
Other values (1054) 1599303
94.3%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 13
 
< 0.1%
3 4896
0.3%
4 1501
 
0.1%
5 8964
0.5%
6 467
 
< 0.1%
9 4
 
< 0.1%
10 1087
 
0.1%
11 9290
0.5%
12 9022
0.5%
ValueCountFrequency (%)
116786 2
 
< 0.1%
110302 361
< 0.1%
101634 1
 
< 0.1%
101362 3
 
< 0.1%
99200 2
 
< 0.1%
91328 3
 
< 0.1%
87883 1
 
< 0.1%
86209 285
< 0.1%
85465 327
< 0.1%
83678 445
< 0.1%

player_current_club_id
Real number (ℝ)

High correlation 

Distinct437
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3979.6011
Minimum3
Maximum110302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:12:29.088462image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile29
Q1331
median903
Q32696
95-th percentile19771
Maximum110302
Range110299
Interquartile range (IQR)2365

Descriptive statistics

Standard deviation10806.848
Coefficient of variation (CV)2.7155607
Kurtosis30.513812
Mean3979.6011
Median Absolute Deviation (MAD)713
Skewness5.100011
Sum6.7503347 × 109
Variance1.1678797 × 108
MonotonicityNot monotonic
2025-03-12T21:12:29.151648image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 11702
 
0.7%
683 10785
 
0.6%
1091 10266
 
0.6%
150 10255
 
0.6%
46 10109
 
0.6%
141 10108
 
0.6%
13 9981
 
0.6%
2381 9958
 
0.6%
368 9628
 
0.6%
589 9498
 
0.6%
Other values (427) 1593944
94.0%
ValueCountFrequency (%)
3 4765
0.3%
4 987
 
0.1%
5 8638
0.5%
6 618
 
< 0.1%
10 1190
 
0.1%
11 7593
0.4%
12 8008
0.5%
13 9981
0.6%
15 7461
0.4%
16 6481
0.4%
ValueCountFrequency (%)
110302 2320
0.1%
86209 729
 
< 0.1%
85465 1892
 
0.1%
83678 769
 
< 0.1%
75231 824
 
< 0.1%
71985 1222
 
0.1%
68608 1977
0.1%
63007 2555
0.2%
61825 2392
0.1%
60949 4824
0.3%

date
Date

Distinct3717
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
Minimum2012-07-03 00:00:00
Maximum2025-03-10 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-12T21:12:29.211525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:29.276571image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct25119
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size126.2 MiB
2025-03-12T21:12:29.422940image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length29
Mean length13.473428
Min length2

Characters and Unicode

Total characters22854086
Distinct characters121
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2111 ?
Unique (%)0.1%

Sample

1st rowAurélien Joachim
2nd rowRuslan Abyshov
3rd rowSander Puri
4th rowVegar Hedenstad
5th rowMarkus Henriksen
ValueCountFrequency (%)
de 14398
 
0.4%
van 14237
 
0.4%
david 11680
 
0.3%
lucas 9957
 
0.3%
thomas 8444
 
0.3%
kevin 7908
 
0.2%
daniel 7764
 
0.2%
andré 7245
 
0.2%
pedro 7121
 
0.2%
joão 7084
 
0.2%
Other values (23832) 3280994
97.2%
2025-03-12T21:12:29.627452image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2268340
 
9.9%
e 1733616
 
7.6%
1680598
 
7.4%
i 1628621
 
7.1%
o 1530209
 
6.7%
n 1496563
 
6.5%
r 1419771
 
6.2%
l 991297
 
4.3%
s 990515
 
4.3%
t 678702
 
3.0%
Other values (111) 8435854
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22854086
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2268340
 
9.9%
e 1733616
 
7.6%
1680598
 
7.4%
i 1628621
 
7.1%
o 1530209
 
6.7%
n 1496563
 
6.5%
r 1419771
 
6.2%
l 991297
 
4.3%
s 990515
 
4.3%
t 678702
 
3.0%
Other values (111) 8435854
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22854086
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2268340
 
9.9%
e 1733616
 
7.6%
1680598
 
7.4%
i 1628621
 
7.1%
o 1530209
 
6.7%
n 1496563
 
6.5%
r 1419771
 
6.2%
l 991297
 
4.3%
s 990515
 
4.3%
t 678702
 
3.0%
Other values (111) 8435854
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22854086
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2268340
 
9.9%
e 1733616
 
7.6%
1680598
 
7.4%
i 1628621
 
7.1%
o 1530209
 
6.7%
n 1496563
 
6.5%
r 1419771
 
6.2%
l 991297
 
4.3%
s 990515
 
4.3%
t 678702
 
3.0%
Other values (111) 8435854
36.9%

competition_id
Categorical

High correlation 

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size97.0 MiB
IT1
139849 
ES1
138147 
GB1
135091 
FR1
131229 
TR1
119614 
Other values (38)
1032304 

Length

Max length4
Median length3
Mean length2.9325748
Min length2

Characters and Unicode

Total characters4974333
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLQ
2nd rowELQ
3rd rowELQ
4th rowELQ
5th rowELQ

Common Values

ValueCountFrequency (%)
IT1 139849
 
8.2%
ES1 138147
 
8.1%
GB1 135091
 
8.0%
FR1 131229
 
7.7%
TR1 119614
 
7.1%
L1 112441
 
6.6%
NL1 107748
 
6.4%
PO1 107305
 
6.3%
BE1 92929
 
5.5%
RU1 85204
 
5.0%
Other values (33) 526677
31.0%

Length

2025-03-12T21:12:29.698520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
it1 139849
 
8.2%
es1 138147
 
8.1%
gb1 135091
 
8.0%
fr1 131229
 
7.7%
tr1 119614
 
7.1%
l1 112441
 
6.6%
nl1 107748
 
6.4%
po1 107305
 
6.3%
be1 92929
 
5.5%
ru1 85204
 
5.0%
Other values (33) 526677
31.0%

Most occurring characters

ValueCountFrequency (%)
1 1451827
29.2%
R 532295
 
10.7%
L 353586
 
7.1%
E 302983
 
6.1%
T 269688
 
5.4%
B 241271
 
4.9%
G 232914
 
4.7%
S 222972
 
4.5%
C 183736
 
3.7%
P 176424
 
3.5%
Other values (10) 1006637
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4974333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1451827
29.2%
R 532295
 
10.7%
L 353586
 
7.1%
E 302983
 
6.1%
T 269688
 
5.4%
B 241271
 
4.9%
G 232914
 
4.7%
S 222972
 
4.5%
C 183736
 
3.7%
P 176424
 
3.5%
Other values (10) 1006637
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4974333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1451827
29.2%
R 532295
 
10.7%
L 353586
 
7.1%
E 302983
 
6.1%
T 269688
 
5.4%
B 241271
 
4.9%
G 232914
 
4.7%
S 222972
 
4.5%
C 183736
 
3.7%
P 176424
 
3.5%
Other values (10) 1006637
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4974333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1451827
29.2%
R 532295
 
10.7%
L 353586
 
7.1%
E 302983
 
6.1%
T 269688
 
5.4%
B 241271
 
4.9%
G 232914
 
4.7%
S 222972
 
4.5%
C 183736
 
3.7%
P 176424
 
3.5%
Other values (10) 1006637
20.2%

goals
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.095945489
Minimum0
Maximum6
Zeros1550716
Zeros (%)91.4%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:12:29.746146image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.33104299
Coefficient of variation (CV)3.4503236
Kurtosis17.871832
Mean0.095945489
Median Absolute Deviation (MAD)0
Skewness3.8743377
Sum162746
Variance0.10958946
MonotonicityNot monotonic
2025-03-12T21:12:29.791797image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1550716
91.4%
1 130219
 
7.7%
2 13566
 
0.8%
3 1562
 
0.1%
4 147
 
< 0.1%
5 23
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 1550716
91.4%
1 130219
 
7.7%
2 13566
 
0.8%
3 1562
 
0.1%
4 147
 
< 0.1%
5 23
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 23
 
< 0.1%
4 147
 
< 0.1%
3 1562
 
0.1%
2 13566
 
0.8%
1 130219
 
7.7%
0 1550716
91.4%

assists
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.075450085
Minimum0
Maximum6
Zeros1577530
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:12:29.836344image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.28542405
Coefficient of variation (CV)3.782952
Kurtosis18.381554
Mean0.075450085
Median Absolute Deviation (MAD)0
Skewness4.0511294
Sum127981
Variance0.081466887
MonotonicityNot monotonic
2025-03-12T21:12:29.881880image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1577530
93.0%
1 110036
 
6.5%
2 8100
 
0.5%
3 530
 
< 0.1%
4 36
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 1577530
93.0%
1 110036
 
6.5%
2 8100
 
0.5%
3 530
 
< 0.1%
4 36
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 1
 
< 0.1%
4 36
 
< 0.1%
3 530
 
< 0.1%
2 8100
 
0.5%
1 110036
 
6.5%
0 1577530
93.0%

minutes_played
Real number (ℝ)

Distinct122
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.089002
Minimum1
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:12:29.937496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q145
median90
Q390
95-th percentile90
Maximum148
Range147
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.981585
Coefficient of variation (CV)0.43395597
Kurtosis-0.38137871
Mean69.089002
Median Absolute Deviation (MAD)0
Skewness-1.0701778
Sum1.1719112 × 108
Variance898.89546
MonotonicityNot monotonic
2025-03-12T21:12:30.113721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 907176
53.5%
45 66235
 
3.9%
1 26371
 
1.6%
78 11748
 
0.7%
12 11618
 
0.7%
76 11593
 
0.7%
77 11549
 
0.7%
14 11507
 
0.7%
75 11498
 
0.7%
74 11495
 
0.7%
Other values (112) 615444
36.3%
ValueCountFrequency (%)
1 26371
1.6%
2 7796
 
0.5%
3 8545
 
0.5%
4 9092
 
0.5%
5 9663
 
0.6%
6 10271
 
0.6%
7 10756
0.6%
8 11084
0.7%
9 11253
0.7%
10 11241
0.7%
ValueCountFrequency (%)
148 1
 
< 0.1%
135 2
 
< 0.1%
120 8142
0.5%
119 30
 
< 0.1%
118 30
 
< 0.1%
117 25
 
< 0.1%
116 21
 
< 0.1%
115 43
 
< 0.1%
114 28
 
< 0.1%
113 41
 
< 0.1%

first_name
Text

Missing 

Distinct4327
Distinct (%)0.4%
Missing639134
Missing (%)37.7%
Memory size86.0 MiB
2025-03-12T21:12:30.248322image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length19
Median length17
Mean length5.8893984
Min length2

Characters and Unicode

Total characters6225683
Distinct characters98
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique157 ?
Unique (%)< 0.1%

Sample

1st rowSander
2nd rowVegar
3rd rowMarkus
4th rowPeter
5th rowDušan
ValueCountFrequency (%)
david 6935
 
0.6%
lucas 6198
 
0.6%
kevin 5981
 
0.6%
thomas 5256
 
0.5%
marco 4859
 
0.5%
daniel 4815
 
0.5%
jordan 4376
 
0.4%
josé 4280
 
0.4%
ivan 4178
 
0.4%
aleksandr 4175
 
0.4%
Other values (4250) 1017562
95.2%
2025-03-12T21:12:30.452696image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 678099
 
10.9%
i 513525
 
8.2%
n 474548
 
7.6%
e 467321
 
7.5%
o 431505
 
6.9%
r 424406
 
6.8%
l 295512
 
4.7%
s 284348
 
4.6%
t 193573
 
3.1%
d 168892
 
2.7%
Other values (88) 2293954
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6225683
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 678099
 
10.9%
i 513525
 
8.2%
n 474548
 
7.6%
e 467321
 
7.5%
o 431505
 
6.9%
r 424406
 
6.8%
l 295512
 
4.7%
s 284348
 
4.6%
t 193573
 
3.1%
d 168892
 
2.7%
Other values (88) 2293954
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6225683
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 678099
 
10.9%
i 513525
 
8.2%
n 474548
 
7.6%
e 467321
 
7.5%
o 431505
 
6.9%
r 424406
 
6.8%
l 295512
 
4.7%
s 284348
 
4.6%
t 193573
 
3.1%
d 168892
 
2.7%
Other values (88) 2293954
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6225683
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 678099
 
10.9%
i 513525
 
8.2%
n 474548
 
7.6%
e 467321
 
7.5%
o 431505
 
6.9%
r 424406
 
6.8%
l 295512
 
4.7%
s 284348
 
4.6%
t 193573
 
3.1%
d 168892
 
2.7%
Other values (88) 2293954
36.8%

last_name
Text

Missing 

Distinct11003
Distinct (%)1.0%
Missing639134
Missing (%)37.7%
Memory size88.3 MiB
2025-03-12T21:12:30.596331image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length22
Median length19
Mean length6.9684278
Min length2

Characters and Unicode

Total characters7366325
Distinct characters105
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique624 ?
Unique (%)0.1%

Sample

1st rowPuri
2nd rowHedenstad
3rd rowHenriksen
4th rowAnkersen
5th rowTadić
ValueCountFrequency (%)
de 10214
 
0.9%
van 9376
 
0.9%
silva 3655
 
0.3%
garcía 3177
 
0.3%
el 3144
 
0.3%
martínez 2632
 
0.2%
fernandes 2440
 
0.2%
sánchez 2342
 
0.2%
costa 2310
 
0.2%
rodríguez 2242
 
0.2%
Other values (10996) 1053509
96.2%
2025-03-12T21:12:30.804825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 798714
 
10.8%
e 636956
 
8.6%
i 520750
 
7.1%
o 510787
 
6.9%
r 480763
 
6.5%
n 475599
 
6.5%
s 355810
 
4.8%
l 341257
 
4.6%
t 238013
 
3.2%
u 234199
 
3.2%
Other values (95) 2773477
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7366325
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 798714
 
10.8%
e 636956
 
8.6%
i 520750
 
7.1%
o 510787
 
6.9%
r 480763
 
6.5%
n 475599
 
6.5%
s 355810
 
4.8%
l 341257
 
4.6%
t 238013
 
3.2%
u 234199
 
3.2%
Other values (95) 2773477
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7366325
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 798714
 
10.8%
e 636956
 
8.6%
i 520750
 
7.1%
o 510787
 
6.9%
r 480763
 
6.5%
n 475599
 
6.5%
s 355810
 
4.8%
l 341257
 
4.6%
t 238013
 
3.2%
u 234199
 
3.2%
Other values (95) 2773477
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7366325
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 798714
 
10.8%
e 636956
 
8.6%
i 520750
 
7.1%
o 510787
 
6.9%
r 480763
 
6.5%
n 475599
 
6.5%
s 355810
 
4.8%
l 341257
 
4.6%
t 238013
 
3.2%
u 234199
 
3.2%
Other values (95) 2773477
37.7%

name
Text

Missing 

Distinct13737
Distinct (%)1.3%
Missing639134
Missing (%)37.7%
Memory size99.0 MiB
2025-03-12T21:12:30.945581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length31
Median length27
Mean length13.857826
Min length6

Characters and Unicode

Total characters14649108
Distinct characters112
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique882 ?
Unique (%)0.1%

Sample

1st rowSander Puri
2nd rowVegar Hedenstad
3rd rowMarkus Henriksen
4th rowPeter Ankersen
5th rowDušan Tadić
ValueCountFrequency (%)
de 10214
 
0.5%
van 9376
 
0.4%
david 7246
 
0.3%
lucas 6251
 
0.3%
thomas 6055
 
0.3%
kevin 5981
 
0.3%
marco 4860
 
0.2%
andré 4860
 
0.2%
daniel 4815
 
0.2%
jordan 4376
 
0.2%
Other values (14868) 2099622
97.0%
2025-03-12T21:12:31.146306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1476813
 
10.1%
1106556
 
7.6%
e 1104277
 
7.5%
i 1034275
 
7.1%
n 950147
 
6.5%
o 942292
 
6.4%
r 905169
 
6.2%
s 640158
 
4.4%
l 636769
 
4.3%
t 431586
 
2.9%
Other values (102) 5421066
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14649108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1476813
 
10.1%
1106556
 
7.6%
e 1104277
 
7.5%
i 1034275
 
7.1%
n 950147
 
6.5%
o 942292
 
6.4%
r 905169
 
6.2%
s 640158
 
4.4%
l 636769
 
4.3%
t 431586
 
2.9%
Other values (102) 5421066
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14649108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1476813
 
10.1%
1106556
 
7.6%
e 1104277
 
7.5%
i 1034275
 
7.1%
n 950147
 
6.5%
o 942292
 
6.4%
r 905169
 
6.2%
s 640158
 
4.4%
l 636769
 
4.3%
t 431586
 
2.9%
Other values (102) 5421066
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14649108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1476813
 
10.1%
1106556
 
7.6%
e 1104277
 
7.5%
i 1034275
 
7.1%
n 950147
 
6.5%
o 942292
 
6.4%
r 905169
 
6.2%
s 640158
 
4.4%
l 636769
 
4.3%
t 431586
 
2.9%
Other values (102) 5421066
37.0%

last_season
Real number (ℝ)

High correlation  Missing 

Distinct13
Distinct (%)< 0.1%
Missing639134
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean2022.5416
Minimum2012
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:12:31.204015image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2017
Q12022
median2024
Q32024
95-th percentile2024
Maximum2024
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3321668
Coefficient of variation (CV)0.0011530872
Kurtosis3.0969526
Mean2022.5416
Median Absolute Deviation (MAD)0
Skewness-1.8648831
Sum2.1380287 × 109
Variance5.4390021
MonotonicityNot monotonic
2025-03-12T21:12:31.254507image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2024 602267
35.5%
2023 136924
 
8.1%
2021 74743
 
4.4%
2022 67704
 
4.0%
2020 55313
 
3.3%
2019 34412
 
2.0%
2018 28504
 
1.7%
2017 19381
 
1.1%
2016 14696
 
0.9%
2015 10815
 
0.6%
Other values (3) 12341
 
0.7%
(Missing) 639134
37.7%
ValueCountFrequency (%)
2012 1489
 
0.1%
2013 3820
 
0.2%
2014 7032
 
0.4%
2015 10815
 
0.6%
2016 14696
 
0.9%
2017 19381
 
1.1%
2018 28504
 
1.7%
2019 34412
2.0%
2020 55313
3.3%
2021 74743
4.4%
ValueCountFrequency (%)
2024 602267
35.5%
2023 136924
 
8.1%
2022 67704
 
4.0%
2021 74743
 
4.4%
2020 55313
 
3.3%
2019 34412
 
2.0%
2018 28504
 
1.7%
2017 19381
 
1.1%
2016 14696
 
0.9%
2015 10815
 
0.6%

current_club_id
Real number (ℝ)

High correlation  Missing 

Distinct436
Distinct (%)< 0.1%
Missing639134
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean3957.3817
Minimum3
Maximum110302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:12:31.313655image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile27
Q1281
median800
Q32503
95-th percentile18303
Maximum110302
Range110299
Interquartile range (IQR)2222

Descriptive statistics

Standard deviation11236.602
Coefficient of variation (CV)2.8394031
Kurtosis30.242986
Mean3957.3817
Median Absolute Deviation (MAD)620
Skewness5.1230434
Sum4.1833482 × 109
Variance1.2626122 × 108
MonotonicityNot monotonic
2025-03-12T21:12:31.375561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 7890
 
0.5%
36 7821
 
0.5%
141 7764
 
0.5%
13 7410
 
0.4%
27 7106
 
0.4%
244 6984
 
0.4%
2441 6948
 
0.4%
800 6749
 
0.4%
1091 6735
 
0.4%
418 6664
 
0.4%
Other values (426) 985029
58.1%
(Missing) 639134
37.7%
ValueCountFrequency (%)
3 3396
0.2%
4 588
 
< 0.1%
5 5758
0.3%
6 207
 
< 0.1%
10 869
 
0.1%
11 5284
0.3%
12 6120
0.4%
13 7410
0.4%
15 5583
0.3%
16 5542
0.3%
ValueCountFrequency (%)
110302 1651
0.1%
86209 703
 
< 0.1%
85465 1860
0.1%
83678 547
 
< 0.1%
75231 819
 
< 0.1%
71985 1154
 
0.1%
68608 282
 
< 0.1%
63007 2080
0.1%
61825 1200
 
0.1%
60949 3456
0.2%
Distinct160
Distinct (%)< 0.1%
Missing639134
Missing (%)37.7%
Memory size85.2 MiB
2025-03-12T21:12:31.489245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length24
Median length21
Mean length7.2155737
Min length4

Characters and Unicode

Total characters7627583
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowEstonia
2nd rowNorway
3rd rowNorway
4th rowDenmark
5th rowSerbia
ValueCountFrequency (%)
spain 87042
 
7.9%
france 68495
 
6.2%
netherlands 57445
 
5.2%
england 50233
 
4.5%
germany 48818
 
4.4%
italy 46588
 
4.2%
portugal 41153
 
3.7%
russia 40992
 
3.7%
ukraine 39810
 
3.6%
belgium 38615
 
3.5%
Other values (177) 588458
53.1%
2025-03-12T21:12:31.679355image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 967578
 
12.7%
e 771955
 
10.1%
n 710105
 
9.3%
r 610939
 
8.0%
i 494101
 
6.5%
l 394209
 
5.2%
o 296412
 
3.9%
t 289315
 
3.8%
g 233037
 
3.1%
u 224036
 
2.9%
Other values (46) 2635896
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7627583
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 967578
 
12.7%
e 771955
 
10.1%
n 710105
 
9.3%
r 610939
 
8.0%
i 494101
 
6.5%
l 394209
 
5.2%
o 296412
 
3.9%
t 289315
 
3.8%
g 233037
 
3.1%
u 224036
 
2.9%
Other values (46) 2635896
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7627583
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 967578
 
12.7%
e 771955
 
10.1%
n 710105
 
9.3%
r 610939
 
8.0%
i 494101
 
6.5%
l 394209
 
5.2%
o 296412
 
3.9%
t 289315
 
3.8%
g 233037
 
3.1%
u 224036
 
2.9%
Other values (46) 2635896
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7627583
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 967578
 
12.7%
e 771955
 
10.1%
n 710105
 
9.3%
r 610939
 
8.0%
i 494101
 
6.5%
l 394209
 
5.2%
o 296412
 
3.9%
t 289315
 
3.8%
g 233037
 
3.1%
u 224036
 
2.9%
Other values (46) 2635896
34.6%

date_of_birth
Date

Missing 

Distinct6024
Distinct (%)0.6%
Missing639134
Missing (%)37.7%
Memory size12.9 MiB
Minimum1978-01-28 00:00:00
Maximum2008-09-21 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-12T21:12:31.747929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:31.809780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sub_position
Categorical

High correlation  Missing 

Distinct13
Distinct (%)< 0.1%
Missing639134
Missing (%)37.7%
Memory size109.6 MiB
Centre-Back
190516 
Centre-Forward
149256 
Central Midfield
139159 
Defensive Midfield
93685 
Right-Back
88969 
Other values (8)
395515 

Length

Max length18
Median length14
Mean length13.046192
Min length9

Characters and Unicode

Total characters13791130
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentral Midfield
2nd rowRight-Back
3rd rowCentre-Back
4th rowRight-Back
5th rowRight Winger

Common Values

ValueCountFrequency (%)
Centre-Back 190516
 
11.2%
Centre-Forward 149256
 
8.8%
Central Midfield 139159
 
8.2%
Defensive Midfield 93685
 
5.5%
Right-Back 88969
 
5.2%
Attacking Midfield 76323
 
4.5%
Right Winger 76199
 
4.5%
Goalkeeper 75434
 
4.4%
Left Winger 72740
 
4.3%
Left-Back 71798
 
4.2%
Other values (3) 23021
 
1.4%
(Missing) 639134
37.7%

Length

2025-03-12T21:12:31.865002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
midfield 324232
21.1%
centre-back 190516
12.4%
centre-forward 149256
9.7%
winger 148939
9.7%
central 139159
9.0%
defensive 93685
 
6.1%
right-back 88969
 
5.8%
right 83412
 
5.4%
left 80592
 
5.2%
attacking 76323
 
5.0%
Other values (4) 163144
10.6%

Most occurring characters

ValueCountFrequency (%)
e 1967533
14.3%
i 1147748
 
8.3%
r 1017728
 
7.4%
t 964304
 
7.0%
n 805834
 
5.8%
d 805676
 
5.8%
a 791455
 
5.7%
f 570307
 
4.1%
l 538825
 
3.9%
k 510996
 
3.7%
Other values (21) 4670724
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13791130
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1967533
14.3%
i 1147748
 
8.3%
r 1017728
 
7.4%
t 964304
 
7.0%
n 805834
 
5.8%
d 805676
 
5.8%
a 791455
 
5.7%
f 570307
 
4.1%
l 538825
 
3.9%
k 510996
 
3.7%
Other values (21) 4670724
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13791130
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1967533
14.3%
i 1147748
 
8.3%
r 1017728
 
7.4%
t 964304
 
7.0%
n 805834
 
5.8%
d 805676
 
5.8%
a 791455
 
5.7%
f 570307
 
4.1%
l 538825
 
3.9%
k 510996
 
3.7%
Other values (21) 4670724
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13791130
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1967533
14.3%
i 1147748
 
8.3%
r 1017728
 
7.4%
t 964304
 
7.0%
n 805834
 
5.8%
d 805676
 
5.8%
a 791455
 
5.7%
f 570307
 
4.1%
l 538825
 
3.9%
k 510996
 
3.7%
Other values (21) 4670724
33.9%

position
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing639134
Missing (%)37.7%
Memory size104.1 MiB
Defender
351283 
Midfield
324232 
Attack
306151 
Goalkeeper
75434 

Length

Max length10
Median length8
Mean length7.5634907
Min length6

Characters and Unicode

Total characters7995366
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMidfield
2nd rowDefender
3rd rowDefender
4th rowDefender
5th rowAttack

Common Values

ValueCountFrequency (%)
Defender 351283
20.7%
Midfield 324232
19.1%
Attack 306151
18.0%
Goalkeeper 75434
 
4.4%
(Missing) 639134
37.7%

Length

2025-03-12T21:12:31.920783image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T21:12:31.972856image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
defender 351283
33.2%
midfield 324232
30.7%
attack 306151
29.0%
goalkeeper 75434
 
7.1%

Most occurring characters

ValueCountFrequency (%)
e 1604383
20.1%
d 999747
12.5%
f 675515
8.4%
i 648464
8.1%
t 612302
 
7.7%
r 426717
 
5.3%
l 399666
 
5.0%
k 381585
 
4.8%
a 381585
 
4.8%
D 351283
 
4.4%
Other values (7) 1514119
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7995366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1604383
20.1%
d 999747
12.5%
f 675515
8.4%
i 648464
8.1%
t 612302
 
7.7%
r 426717
 
5.3%
l 399666
 
5.0%
k 381585
 
4.8%
a 381585
 
4.8%
D 351283
 
4.4%
Other values (7) 1514119
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7995366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1604383
20.1%
d 999747
12.5%
f 675515
8.4%
i 648464
8.1%
t 612302
 
7.7%
r 426717
 
5.3%
l 399666
 
5.0%
k 381585
 
4.8%
a 381585
 
4.8%
D 351283
 
4.4%
Other values (7) 1514119
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7995366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1604383
20.1%
d 999747
12.5%
f 675515
8.4%
i 648464
8.1%
t 612302
 
7.7%
r 426717
 
5.3%
l 399666
 
5.0%
k 381585
 
4.8%
a 381585
 
4.8%
D 351283
 
4.4%
Other values (7) 1514119
18.9%

foot
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing639134
Missing (%)37.7%
Memory size101.2 MiB
right
757776 
left
261091 
both
 
38233

Length

Max length5
Median length5
Mean length4.7168442
Min length4

Characters and Unicode

Total characters4986176
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowright
2nd rowright
3rd rowright
4th rowright
5th rowleft

Common Values

ValueCountFrequency (%)
right 757776
44.7%
left 261091
 
15.4%
both 38233
 
2.3%
(Missing) 639134
37.7%

Length

2025-03-12T21:12:32.085621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T21:12:32.179189image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
right 757776
71.7%
left 261091
 
24.7%
both 38233
 
3.6%

Most occurring characters

ValueCountFrequency (%)
t 1057100
21.2%
h 796009
16.0%
r 757776
15.2%
i 757776
15.2%
g 757776
15.2%
l 261091
 
5.2%
e 261091
 
5.2%
f 261091
 
5.2%
b 38233
 
0.8%
o 38233
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4986176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1057100
21.2%
h 796009
16.0%
r 757776
15.2%
i 757776
15.2%
g 757776
15.2%
l 261091
 
5.2%
e 261091
 
5.2%
f 261091
 
5.2%
b 38233
 
0.8%
o 38233
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4986176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1057100
21.2%
h 796009
16.0%
r 757776
15.2%
i 757776
15.2%
g 757776
15.2%
l 261091
 
5.2%
e 261091
 
5.2%
f 261091
 
5.2%
b 38233
 
0.8%
o 38233
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4986176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1057100
21.2%
h 796009
16.0%
r 757776
15.2%
i 757776
15.2%
g 757776
15.2%
l 261091
 
5.2%
e 261091
 
5.2%
f 261091
 
5.2%
b 38233
 
0.8%
o 38233
 
0.8%

height_in_cm
Real number (ℝ)

Missing 

Distinct51
Distinct (%)< 0.1%
Missing639134
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean182.28081
Minimum17
Maximum207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:12:32.267113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile171
Q1178
median183
Q3187
95-th percentile193
Maximum207
Range190
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.7104383
Coefficient of variation (CV)0.03681374
Kurtosis4.6503543
Mean182.28081
Median Absolute Deviation (MAD)5
Skewness-0.25926769
Sum1.9268904 × 108
Variance45.029982
MonotonicityNot monotonic
2025-03-12T21:12:32.376158image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180 74830
 
4.4%
185 71475
 
4.2%
178 61950
 
3.7%
183 60991
 
3.6%
187 56227
 
3.3%
182 53867
 
3.2%
188 53805
 
3.2%
175 50293
 
3.0%
184 50068
 
3.0%
186 49407
 
2.9%
Other values (41) 474187
28.0%
(Missing) 639134
37.7%
ValueCountFrequency (%)
17 5
 
< 0.1%
19 10
 
< 0.1%
159 114
 
< 0.1%
160 28
 
< 0.1%
161 113
 
< 0.1%
162 21
 
< 0.1%
163 1187
0.1%
164 520
 
< 0.1%
165 2150
0.1%
166 2058
0.1%
ValueCountFrequency (%)
207 36
 
< 0.1%
206 137
 
< 0.1%
205 3
 
< 0.1%
204 7
 
< 0.1%
203 89
 
< 0.1%
202 290
 
< 0.1%
201 1649
0.1%
200 754
 
< 0.1%
199 1382
0.1%
198 3062
0.2%
Distinct90
Distinct (%)< 0.1%
Missing639134
Missing (%)37.7%
Memory size12.9 MiB
Minimum2000-05-31 00:00:00
Maximum2034-06-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-12T21:12:32.519601image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:32.620366image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

current_club_domestic_competition_id
Categorical

High correlation  Missing 

Distinct14
Distinct (%)< 0.1%
Missing639134
Missing (%)37.7%
Memory size99.5 MiB
ES1
115155 
IT1
113087 
TR1
110164 
GB1
107354 
L1
79028 
Other values (9)
532312 

Length

Max length4
Median length3
Mean length2.9627717
Min length2

Characters and Unicode

Total characters3131946
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSC1
2nd rowTR1
3rd rowGB1
4th rowDK1
5th rowTR1

Common Values

ValueCountFrequency (%)
ES1 115155
 
6.8%
IT1 113087
 
6.7%
TR1 110164
 
6.5%
GB1 107354
 
6.3%
L1 79028
 
4.7%
FR1 77763
 
4.6%
GR1 74131
 
4.4%
NL1 73550
 
4.3%
RU1 59972
 
3.5%
BE1 57996
 
3.4%
Other values (4) 188900
 
11.1%
(Missing) 639134
37.7%

Length

2025-03-12T21:12:32.730782image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
es1 115155
10.9%
it1 113087
10.7%
tr1 110164
10.4%
gb1 107354
10.2%
l1 79028
7.5%
fr1 77763
7.4%
gr1 74131
 
7.0%
nl1 73550
 
7.0%
ru1 59972
 
5.7%
be1 57996
 
5.5%
Other values (4) 188900
17.9%

Most occurring characters

ValueCountFrequency (%)
1 1057100
33.8%
R 361704
 
11.5%
T 223251
 
7.1%
G 181485
 
5.8%
E 173151
 
5.5%
S 166724
 
5.3%
B 165350
 
5.3%
L 152578
 
4.9%
I 113087
 
3.6%
U 99646
 
3.2%
Other values (7) 437870
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3131946
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1057100
33.8%
R 361704
 
11.5%
T 223251
 
7.1%
G 181485
 
5.8%
E 173151
 
5.5%
S 166724
 
5.3%
B 165350
 
5.3%
L 152578
 
4.9%
I 113087
 
3.6%
U 99646
 
3.2%
Other values (7) 437870
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3131946
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1057100
33.8%
R 361704
 
11.5%
T 223251
 
7.1%
G 181485
 
5.8%
E 173151
 
5.5%
S 166724
 
5.3%
B 165350
 
5.3%
L 152578
 
4.9%
I 113087
 
3.6%
U 99646
 
3.2%
Other values (7) 437870
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3131946
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1057100
33.8%
R 361704
 
11.5%
T 223251
 
7.1%
G 181485
 
5.8%
E 173151
 
5.5%
S 166724
 
5.3%
B 165350
 
5.3%
L 152578
 
4.9%
I 113087
 
3.6%
U 99646
 
3.2%
Other values (7) 437870
14.0%

current_club_name
Text

Missing 

Distinct436
Distinct (%)< 0.1%
Missing639134
Missing (%)37.7%
Memory size115.7 MiB
2025-03-12T21:12:32.940331image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length98
Median length40
Mean length24.152065
Min length4

Characters and Unicode

Total characters25531148
Distinct characters111
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaint Mirren Football Club
2nd rowFatih Karagümrük
3rd rowHull City
4th rowFodbold Club Nordsjælland
5th rowFenerbahçe Spor Kulübü
ValueCountFrequency (%)
club 350397
 
9.8%
football 222762
 
6.2%
de 100050
 
2.8%
kulübü 66231
 
1.9%
s.a.d 62772
 
1.8%
fc 54474
 
1.5%
calcio 53171
 
1.5%
fk 40756
 
1.1%
fútbol 39493
 
1.1%
clube 37072
 
1.0%
Other values (772) 2540371
71.2%
2025-03-12T21:12:33.154637image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2510449
 
9.8%
l 1977389
 
7.7%
o 1886852
 
7.4%
a 1750431
 
6.9%
e 1695807
 
6.6%
i 1379690
 
5.4%
t 1282179
 
5.0%
n 1218303
 
4.8%
r 1081069
 
4.2%
b 1025004
 
4.0%
Other values (101) 9723975
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25531148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2510449
 
9.8%
l 1977389
 
7.7%
o 1886852
 
7.4%
a 1750431
 
6.9%
e 1695807
 
6.6%
i 1379690
 
5.4%
t 1282179
 
5.0%
n 1218303
 
4.8%
r 1081069
 
4.2%
b 1025004
 
4.0%
Other values (101) 9723975
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25531148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2510449
 
9.8%
l 1977389
 
7.7%
o 1886852
 
7.4%
a 1750431
 
6.9%
e 1695807
 
6.6%
i 1379690
 
5.4%
t 1282179
 
5.0%
n 1218303
 
4.8%
r 1081069
 
4.2%
b 1025004
 
4.0%
Other values (101) 9723975
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25531148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2510449
 
9.8%
l 1977389
 
7.7%
o 1886852
 
7.4%
a 1750431
 
6.9%
e 1695807
 
6.6%
i 1379690
 
5.4%
t 1282179
 
5.0%
n 1218303
 
4.8%
r 1081069
 
4.2%
b 1025004
 
4.0%
Other values (101) 9723975
38.1%

market_value_in_eur
Real number (ℝ)

High correlation  Missing 

Distinct122
Distinct (%)< 0.1%
Missing639134
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean6121123.6
Minimum10000
Maximum2 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:12:33.225983image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile100000
Q1400000
median1300000
Q35000000
95-th percentile30000000
Maximum2 × 108
Range1.9999 × 108
Interquartile range (IQR)4600000

Descriptive statistics

Standard deviation13425977
Coefficient of variation (CV)2.1933844
Kurtosis41.533419
Mean6121123.6
Median Absolute Deviation (MAD)1100000
Skewness5.2115042
Sum6.4706398 × 1012
Variance1.8025686 × 1014
MonotonicityNot monotonic
2025-03-12T21:12:33.294033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300000 43079
 
2.5%
1500000 42349
 
2.5%
200000 40191
 
2.4%
1000000 39193
 
2.3%
500000 36400
 
2.1%
2000000 36343
 
2.1%
400000 35733
 
2.1%
3000000 33834
 
2.0%
100000 33616
 
2.0%
2500000 32336
 
1.9%
Other values (112) 684026
40.3%
(Missing) 639134
37.7%
ValueCountFrequency (%)
10000 862
 
0.1%
20000 22
 
< 0.1%
25000 7712
 
0.5%
50000 16702
1.0%
75000 10887
 
0.6%
100000 33616
2.0%
125000 6030
 
0.4%
150000 26949
1.6%
175000 9548
 
0.6%
200000 40191
2.4%
ValueCountFrequency (%)
200000000 229
 
< 0.1%
180000000 210
 
< 0.1%
160000000 365
< 0.1%
150000000 242
 
< 0.1%
140000000 675
< 0.1%
130000000 436
< 0.1%
110000000 618
< 0.1%
100000000 320
< 0.1%
90000000 488
< 0.1%
85000000 425
< 0.1%

highest_market_value_in_eur
Real number (ℝ)

High correlation  Missing 

Distinct156
Distinct (%)< 0.1%
Missing639134
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean14123050
Minimum10000
Maximum2 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2025-03-12T21:12:33.513621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile500000
Q11800000
median5500000
Q318000000
95-th percentile55000000
Maximum2 × 108
Range1.9999 × 108
Interquartile range (IQR)16200000

Descriptive statistics

Standard deviation21127385
Coefficient of variation (CV)1.4959506
Kurtosis13.286691
Mean14123050
Median Absolute Deviation (MAD)4600000
Skewness3.0841609
Sum1.4929476 × 1013
Variance4.4636638 × 1014
MonotonicityNot monotonic
2025-03-12T21:12:33.579586image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000000 41241
 
2.4%
10000000 40977
 
2.4%
3000000 37812
 
2.2%
2000000 37094
 
2.2%
4000000 36100
 
2.1%
1500000 35633
 
2.1%
2500000 34781
 
2.1%
1000000 32937
 
1.9%
20000000 28661
 
1.7%
15000000 28157
 
1.7%
Other values (146) 703707
41.5%
(Missing) 639134
37.7%
ValueCountFrequency (%)
10000 4
 
< 0.1%
25000 34
 
< 0.1%
50000 300
 
< 0.1%
75000 223
 
< 0.1%
100000 1044
0.1%
125000 282
 
< 0.1%
150000 1325
0.1%
175000 405
 
< 0.1%
200000 2522
0.1%
225000 748
 
< 0.1%
ValueCountFrequency (%)
200000000 594
 
< 0.1%
180000000 210
 
< 0.1%
160000000 538
 
< 0.1%
150000000 2849
0.2%
140000000 388
 
< 0.1%
130000000 698
 
< 0.1%
120000000 878
 
0.1%
110000000 1433
 
0.1%
100000000 5083
0.3%
90000000 5390
0.3%

Interactions

2025-03-12T21:12:18.817994image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:03.460042image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:04.985398image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:06.389570image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:08.179563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:09.551234image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:10.878427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:12.173420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:13.518571image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:14.873171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:16.039788image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:17.570476image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:18.923623image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:03.664095image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:05.110529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:06.505071image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:08.298121image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:09.664388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:10.990572image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:12.294389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:13.619563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:14.970161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:16.144154image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-12T21:12:19.026500image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:03.819457image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-12T21:12:11.105048image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:12.413645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-03-12T21:12:17.458215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:18.709919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-03-12T21:12:33.632706image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Unnamed: 0assistscompetition_idcurrent_club_domestic_competition_idcurrent_club_idfootgoalsheight_in_cmhighest_market_value_in_eurlast_seasonmarket_value_in_eurminutes_playedplayer_club_idplayer_current_club_idplayer_idpositionsub_position
Unnamed: 01.000-0.0110.0600.046-0.0440.031-0.0060.0270.0510.4760.374-0.1110.014-0.0570.6400.0120.019
assists-0.0111.0000.0160.013-0.0350.0160.072-0.0850.0740.0210.0520.055-0.035-0.035-0.0090.0650.071
competition_id0.0600.0161.0000.7220.2110.0630.0160.0690.1600.0750.0990.0880.1660.1930.0690.0300.054
current_club_domestic_competition_id0.0460.0130.7221.0000.2460.0660.0110.0840.1600.0690.1330.0230.1780.2460.0700.0420.062
current_club_id-0.044-0.0350.2110.2461.0000.026-0.037-0.053-0.413-0.176-0.392-0.0230.5961.000-0.0110.0280.034
foot0.0310.0160.0630.0660.0261.0000.0120.0370.0420.0490.0380.0240.0220.0260.0450.1160.367
goals-0.0060.0720.0160.011-0.0370.0121.000-0.0260.0940.0190.0680.050-0.031-0.036-0.0060.1260.099
height_in_cm0.027-0.0850.0690.084-0.0530.037-0.0261.0000.0290.0630.0380.182-0.048-0.053-0.0300.2180.290
highest_market_value_in_eur0.0510.0740.1600.160-0.4130.0420.0940.0291.0000.3970.7520.062-0.405-0.413-0.0320.0780.075
last_season0.4760.0210.0750.069-0.1760.0490.0190.0630.3971.0000.5670.029-0.126-0.1760.3650.0440.043
market_value_in_eur0.3740.0520.0990.133-0.3920.0380.0680.0380.7520.5671.0000.007-0.300-0.3920.4430.0600.052
minutes_played-0.1110.0550.0880.023-0.0230.0240.0500.1820.0620.0290.0071.000-0.003-0.024-0.1620.2440.147
player_club_id0.014-0.0350.1660.1780.5960.022-0.031-0.048-0.405-0.126-0.300-0.0031.0000.6180.0800.0120.020
player_current_club_id-0.057-0.0350.1930.2461.0000.026-0.036-0.053-0.413-0.176-0.392-0.0240.6181.0000.0010.0280.035
player_id0.640-0.0090.0690.070-0.0110.045-0.006-0.030-0.0320.3650.443-0.1620.0800.0011.0000.0800.062
position0.0120.0650.0300.0420.0280.1160.1260.2180.0780.0440.0600.2440.0120.0280.0801.0001.000
sub_position0.0190.0710.0540.0620.0340.3670.0990.2900.0750.0430.0520.1470.0200.0350.0621.0001.000

Missing values

2025-03-12T21:12:20.655715image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-12T21:12:22.890317image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-12T21:12:27.352336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0player_idplayer_club_idplayer_current_club_iddateplayer_namecompetition_idgoalsassistsminutes_playedfirst_namelast_namenamelast_seasoncurrent_club_idcountry_of_citizenshipdate_of_birthsub_positionpositionfootheight_in_cmcontract_expiration_datecurrent_club_domestic_competition_idcurrent_club_namemarket_value_in_eurhighest_market_value_in_eur
00380048532352012-07-03Aurélien JoachimCLQ2090NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1179232884126982012-07-05Ruslan AbyshovELQ0090NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
224279262514652012-07-05Sander PuriELQ0045SanderPuriSander Puri2012.0465.0Estonia1988-05-07 00:00:00Central MidfieldMidfieldright177.02023-12-31 00:00:00SC1Saint Mirren Football Club100000.0600000.0
3373333127466462012-07-05Vegar HedenstadELQ0090VegarHedenstadVegar Hedenstad2021.06646.0Norway1991-06-26 00:00:00Right-BackDefenderright178.02024-12-31 00:00:00TR1Fatih Karagümrük350000.01500000.0
4412201119530082012-07-05Markus HenriksenELQ0190MarkusHenriksenMarkus Henriksen2016.03008.0Norway1992-07-25 00:00:00Centre-BackDefenderright187.02024-12-31 00:00:00GB1Hull City800000.05000000.0
5514688919527782012-07-05Peter AnkersenELQ0090PeterAnkersenPeter Ankersen2024.02778.0Denmark1990-09-22 00:00:00Right-BackDefenderright180.02026-06-30 00:00:00DK1Fodbold Club Nordsjælland250000.03000000.0
662871628271852012-07-05Adi AdilovicELQ0090NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7769445282197712012-07-05Ivan SesarELQ0190NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
88194093172002012-07-05Willem JanssenELQ0045NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99300033173172012-07-05Wout BramaELQ0090NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Unnamed: 0player_idplayer_club_idplayer_current_club_iddateplayer_namecompetition_idgoalsassistsminutes_playedfirst_namelast_namenamelast_seasoncurrent_club_idcountry_of_citizenshipdate_of_birthsub_positionpositionfootheight_in_cmcontract_expiration_datecurrent_club_domestic_competition_idcurrent_club_namemarket_value_in_eurhighest_market_value_in_eur
16962241696224342151257825782025-03-10Daniels BalodisSFA0090NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
16962251696225359199257825782025-03-10Andy FisherSFA0090AndyFisherAndy Fisher2024.02578.0England1998-02-12 00:00:00GoalkeeperGoalkeeperright189.02025-05-31 00:00:00SC1Saint Johnstone Football Club900000.01000000.0
16962261696226430976257825782025-03-10Jonathan SvedbergSFA0069NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
16962271696227503731257825782025-03-10Stephen Duke-McKennaSFA0045StephenDuke-McKennaStephen Duke-McKenna2024.02578.0Guyana2000-08-17 00:00:00Right MidfieldMidfieldright170.02025-05-31 00:00:00SC1Saint Johnstone Football Club150000.0150000.0
16962281696228700144257825782025-03-10Makenzie KirkSFA0090NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
16962291696229741267257825782025-03-10Sam CurtisSFA0090SamCurtisSam Curtis2024.02578.0Ireland2005-12-01 00:00:00Right-BackDefenderright185.02025-05-31 00:00:00SC1Saint Johnstone Football Club175000.0175000.0
16962301696230796307257825782025-03-10Zach MitchellSFA0090NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1696231169623180339257825782025-03-10Graham CareySFA1021GrahamCareyGraham Carey2024.02578.0Ireland1989-05-20 00:00:00Left MidfieldMidfieldleft183.02025-05-31 00:00:00SC1Saint Johnstone Football Club150000.0700000.0
169623216962329092541241432025-03-10Macaulay TaitSFA0090MacaulayTaitMacaulay Tait2024.043.0Scotland2005-08-27 00:00:00Central MidfieldMidfieldleft170.02028-05-31 00:00:00SC1Heart of Midlothian Football Club200000.0200000.0
1696233169623391854257825782025-03-10Barry DouglasSFA0090BarryDouglasBarry Douglas2024.02578.0Scotland1989-09-04 00:00:00Left-BackDefenderleft176.02025-05-31 00:00:00SC1Saint Johnstone Football Club100000.03500000.0